AI 中文总结
本研究通过随机联合实验发现，在公民大会中，政治立场和气候关注等特定情境属性对感知代表性的影响强于人口统计属性，且相关配额无法替代明确分层，预测模型可辅助属性选择。
AI 中文摘要
公民大会是旨在形成人口缩影的审议机构。组织者依赖于基于配额的 stratification（分层），并且必须决定哪些属性定义与公众的相似性。然而，满足每一个配额仍然可能留下一个公民重视的维度未被代表。我们通过随机 conjoint（联合）实验研究了这个属性选择问题，涉及通用型和气候聚焦型大会。我们发现人口统计属性对感知代表性很重要，但政治立场和特定情境属性（如气候关注）具有更强的影响。当两者在气候聚焦环境中同时展示时，每个属性仍然具有影响力，其中政治立场具有更大的估计边际效应。我们还考察了相关分层属性的遗漏。基于人口统计进行分层的小组，即使包含政治立场，其气候关注分布与观察到的总体匹配程度并不优于均匀随机样本。这些结果表明，在相关的特定主题属性上的代表性不能总是通过相关的人口统计或政治配额来恢复，因此可能需要明确的分层。最后，我们询问是否可以从观察到的概况中学习代表性偏好。我们比较了预测模型，从简单、可解释的匹配规则到学习度量和受访者条件效用模型。两个学习模型都以相当的准确性预测了被排除在训练之外的受访者的选择，揭示了可泛化的结构，但没有完全捕捉这些判断。总之，这些发现指导公民大会中的属性选择：设计者应考虑政治和特定主题维度以及人口统计，避免假设相关代理保护被遗漏的维度，并使用预测模型来诊断概况如何塑造代表性选择。
英文摘要
Citizens' assemblies are deliberative bodies intended to form a microcosm of the population. Organizers rely on quota-based stratification and must decide which attributes define resemblance to the public. Yet meeting every quota can still leave a dimension citizens value unrepresented. We study this attribute-selection problem in general-purpose and climate-focused assemblies through randomized conjoint experiments. We find that demographic attributes matter for perceived representation, but political alignment and context-specific attributes such as climate concern exert a stronger influence. When both are shown in a climate-focused setting, each remains influential, with political alignment having the larger estimated marginal effect. We also examine the omission of a relevant stratification attribute. Panels stratified on demographics, even with political alignment included, match the observed pool's climate-concern distribution no better than uniform random samples. These results suggest that representation on a relevant topic-specific attribute cannot always be recovered through correlated demographic or political quotas, and may therefore require explicit stratification. Finally, we ask whether representation preferences can be learned from the observed profiles. We compare predictive models, from simple, interpretable matching rules to a learned metric and a respondent-conditioned utility model. Both learned models predict choices for respondents excluded from training with substantial accuracy, revealing generalizable structure without fully capturing these judgments. Together, these findings guide attribute selection in citizens' assemblies: designers should consider political and topic-specific dimensions alongside demographics, avoid assuming correlated proxies protect omitted dimensions, and use predictive models to diagnose how profiles shape representation choices.
Comments27 pages, 10 figures
